Data Ingestion Strategies with Snowflake — Choosing the Right Path for Every Source

Batch loads, Snowpipe, streaming, change data capture, managed connectors, query-in-place — Snowflake gives you many ways to get data in. The hard part is knowing which to reach for, and when. 

In this session we cut through the options with a simple, repeatable decision framework that maps any source and latency requirement to the right Snowflake ingestion capability — including the cases where the smartest move is not to move the data at all.

What you'll learn:

• A repeatable framework for matching a source + latency requirement to the right ingestion capability.
• When batch loading is the right answer — and when it becomes an anti-pattern.
• How to choose the right streaming pattern.
• When Openflow managed connectors (database CDC or SaaS) are better than building your own pipeline.
• When not to ingest at all — querying in place and sharing instead of copying (zero-copy integrations).

What we'll cover:

• The ingestion patterns: batch → continuous → streaming.
• Batch and bulk loading for scheduled, high-volume data.
• Continuous file ingestion with Snowpipe.
• Real-time, row-level streaming.
• Openflow: managed connectors and change data capture.
• Query-in-place and zero-copy data sharing.
• A capability selector you can take back to your team.

Who should attend: Enterprise and data architects, data-engineering team leads, and data engineers who want a clear mental model for ingestion decisions — no click-by-click tutorials, just the when and what.